/usr/lib/python3/dist-packages/sklearn/utils/sparsefuncs.py is in python3-sklearn 0.17.0-4.
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# Thomas Unterthiner
# Giorgio Patrini
#
# License: BSD 3 clause
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max, bincount
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
def _raise_typeerror(X):
"""Raises a TypeError if X is not a CSR or CSC matrix"""
input_type = X.format if sp.issparse(X) else type(X)
err = "Expected a CSR or CSC sparse matrix, got %s." % input_type
raise TypeError(err)
def _raise_error_wrong_axis(axis):
if axis not in (0, 1):
raise ValueError(
"Unknown axis value: %d. Use 0 for rows, or 1 for columns" % axis)
def inplace_csr_column_scale(X, scale):
"""Inplace column scaling of a CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : CSR matrix with shape (n_samples, n_features)
Matrix to normalize using the variance of the features.
scale : float array with shape (n_features,)
Array of precomputed feature-wise values to use for scaling.
"""
assert scale.shape[0] == X.shape[1]
X.data *= scale.take(X.indices, mode='clip')
def inplace_csr_row_scale(X, scale):
""" Inplace row scaling of a CSR matrix.
Scale each sample of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : CSR sparse matrix, shape (n_samples, n_features)
Matrix to be scaled.
scale : float array with shape (n_samples,)
Array of precomputed sample-wise values to use for scaling.
"""
assert scale.shape[0] == X.shape[0]
X.data *= np.repeat(scale, np.diff(X.indptr))
def mean_variance_axis(X, axis):
"""Compute mean and variance along an axix on a CSR or CSC matrix
Parameters
----------
X: CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
axis: int (either 0 or 1)
Axis along which the axis should be computed.
Returns
-------
means: float array with shape (n_features,)
Feature-wise means
variances: float array with shape (n_features,)
Feature-wise variances
"""
_raise_error_wrong_axis(axis)
if isinstance(X, sp.csr_matrix):
if axis == 0:
return _csr_mean_var_axis0(X)
else:
return _csc_mean_var_axis0(X.T)
elif isinstance(X, sp.csc_matrix):
if axis == 0:
return _csc_mean_var_axis0(X)
else:
return _csr_mean_var_axis0(X.T)
else:
_raise_typeerror(X)
def incr_mean_variance_axis(X, axis, last_mean, last_var, last_n):
"""Compute incremental mean and variance along an axix on a CSR or
CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initilized to 0-arrays of the proper size, i.e.
the number of features in X. last_n is the number of samples encountered
until now.
Parameters
----------
X: CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
axis: int (either 0 or 1)
Axis along which the axis should be computed.
last_mean: float array with shape (n_features,)
Array of feature-wise means to update with the new data X.
last_var: float array with shape (n_features,)
Array of feature-wise var to update with the new data X.
last_n: int
Number of samples seen so far, excluded X.
Returns
-------
means: float array with shape (n_features,)
Updated feature-wise means.
variances: float array with shape (n_features,)
Updated feature-wise variances.
n: int
Updated number of seen samples.
"""
_raise_error_wrong_axis(axis)
if isinstance(X, sp.csr_matrix):
if axis == 0:
return _incr_mean_var_axis0(X, last_mean=last_mean,
last_var=last_var, last_n=last_n)
else:
return _incr_mean_var_axis0(X.T, last_mean=last_mean,
last_var=last_var, last_n=last_n)
elif isinstance(X, sp.csc_matrix):
if axis == 0:
return _incr_mean_var_axis0(X, last_mean=last_mean,
last_var=last_var, last_n=last_n)
else:
return _incr_mean_var_axis0(X.T, last_mean=last_mean,
last_var=last_var, last_n=last_n)
else:
_raise_typeerror(X)
def inplace_column_scale(X, scale):
"""Inplace column scaling of a CSC/CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X: CSC or CSR matrix with shape (n_samples, n_features)
Matrix to normalize using the variance of the features.
scale: float array with shape (n_features,)
Array of precomputed feature-wise values to use for scaling.
"""
if isinstance(X, sp.csc_matrix):
inplace_csr_row_scale(X.T, scale)
elif isinstance(X, sp.csr_matrix):
inplace_csr_column_scale(X, scale)
else:
_raise_typeerror(X)
def inplace_row_scale(X, scale):
""" Inplace row scaling of a CSR or CSC matrix.
Scale each row of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : CSR or CSC sparse matrix, shape (n_samples, n_features)
Matrix to be scaled.
scale : float array with shape (n_features,)
Array of precomputed sample-wise values to use for scaling.
"""
if isinstance(X, sp.csc_matrix):
inplace_csr_column_scale(X.T, scale)
elif isinstance(X, sp.csr_matrix):
inplace_csr_row_scale(X, scale)
else:
_raise_typeerror(X)
def inplace_swap_row_csc(X, m, n):
"""
Swaps two rows of a CSC matrix in-place.
Parameters
----------
X: scipy.sparse.csc_matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.
m: int
Index of the row of X to be swapped.
n: int
Index of the row of X to be swapped.
"""
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")
if m < 0:
m += X.shape[0]
if n < 0:
n += X.shape[0]
m_mask = X.indices == m
X.indices[X.indices == n] = m
X.indices[m_mask] = n
def inplace_swap_row_csr(X, m, n):
"""
Swaps two rows of a CSR matrix in-place.
Parameters
----------
X: scipy.sparse.csr_matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.
m: int
Index of the row of X to be swapped.
n: int
Index of the row of X to be swapped.
"""
for t in [m, n]:
if isinstance(t, np.ndarray):
raise TypeError("m and n should be valid integers")
if m < 0:
m += X.shape[0]
if n < 0:
n += X.shape[0]
# The following swapping makes life easier since m is assumed to be the
# smaller integer below.
if m > n:
m, n = n, m
indptr = X.indptr
m_start = indptr[m]
m_stop = indptr[m + 1]
n_start = indptr[n]
n_stop = indptr[n + 1]
nz_m = m_stop - m_start
nz_n = n_stop - n_start
if nz_m != nz_n:
# Modify indptr first
X.indptr[m + 2:n] += nz_n - nz_m
X.indptr[m + 1] = m_start + nz_n
X.indptr[n] = n_stop - nz_m
X.indices = np.concatenate([X.indices[:m_start],
X.indices[n_start:n_stop],
X.indices[m_stop:n_start],
X.indices[m_start:m_stop],
X.indices[n_stop:]])
X.data = np.concatenate([X.data[:m_start],
X.data[n_start:n_stop],
X.data[m_stop:n_start],
X.data[m_start:m_stop],
X.data[n_stop:]])
def inplace_swap_row(X, m, n):
"""
Swaps two rows of a CSC/CSR matrix in-place.
Parameters
----------
X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
Matrix whose two rows are to be swapped.
m: int
Index of the row of X to be swapped.
n: int
Index of the row of X to be swapped.
"""
if isinstance(X, sp.csc_matrix):
return inplace_swap_row_csc(X, m, n)
elif isinstance(X, sp.csr_matrix):
return inplace_swap_row_csr(X, m, n)
else:
_raise_typeerror(X)
def inplace_swap_column(X, m, n):
"""
Swaps two columns of a CSC/CSR matrix in-place.
Parameters
----------
X : CSR or CSC sparse matrix, shape=(n_samples, n_features)
Matrix whose two columns are to be swapped.
m: int
Index of the column of X to be swapped.
n : int
Index of the column of X to be swapped.
"""
if m < 0:
m += X.shape[1]
if n < 0:
n += X.shape[1]
if isinstance(X, sp.csc_matrix):
return inplace_swap_row_csr(X, m, n)
elif isinstance(X, sp.csr_matrix):
return inplace_swap_row_csc(X, m, n)
else:
_raise_typeerror(X)
def min_max_axis(X, axis):
"""Compute minimum and maximum along an axis on a CSR or CSC matrix
Parameters
----------
X : CSR or CSC sparse matrix, shape (n_samples, n_features)
Input data.
axis: int (either 0 or 1)
Axis along which the axis should be computed.
Returns
-------
mins: float array with shape (n_features,)
Feature-wise minima
maxs: float array with shape (n_features,)
Feature-wise maxima
"""
if isinstance(X, sp.csr_matrix) or isinstance(X, sp.csc_matrix):
return sparse_min_max(X, axis=axis)
else:
_raise_typeerror(X)
def count_nonzero(X, axis=None, sample_weight=None):
"""A variant of X.getnnz() with extension to weighting on axis 0
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : CSR sparse matrix, shape = (n_samples, n_labels)
Input data.
axis : None, 0 or 1
The axis on which the data is aggregated.
sample_weight : array, shape = (n_samples,), optional
Weight for each row of X.
"""
if axis == -1:
axis = 1
elif axis == -2:
axis = 0
elif X.format != 'csr':
raise TypeError('Expected CSR sparse format, got {0}'.format(X.format))
# We rely here on the fact that np.diff(Y.indptr) for a CSR
# will return the number of nonzero entries in each row.
# A bincount over Y.indices will return the number of nonzeros
# in each column. See ``csr_matrix.getnnz`` in scipy >= 0.14.
if axis is None:
if sample_weight is None:
return X.nnz
else:
return np.dot(np.diff(X.indptr), sample_weight)
elif axis == 1:
out = np.diff(X.indptr)
if sample_weight is None:
return out
return out * sample_weight
elif axis == 0:
if sample_weight is None:
return bincount(X.indices, minlength=X.shape[1])
else:
weights = np.repeat(sample_weight, np.diff(X.indptr))
return bincount(X.indices, minlength=X.shape[1],
weights=weights)
else:
raise ValueError('Unsupported axis: {0}'.format(axis))
def _get_median(data, n_zeros):
"""Compute the median of data with n_zeros additional zeros.
This function is used to support sparse matrices; it modifies data in-place
"""
n_elems = len(data) + n_zeros
if not n_elems:
return np.nan
n_negative = np.count_nonzero(data < 0)
middle, is_odd = divmod(n_elems, 2)
data.sort()
if is_odd:
return _get_elem_at_rank(middle, data, n_negative, n_zeros)
return (_get_elem_at_rank(middle - 1, data, n_negative, n_zeros) +
_get_elem_at_rank(middle, data, n_negative, n_zeros)) / 2.
def _get_elem_at_rank(rank, data, n_negative, n_zeros):
"""Find the value in data augmented with n_zeros for the given rank"""
if rank < n_negative:
return data[rank]
if rank - n_negative < n_zeros:
return 0
return data[rank - n_zeros]
def csc_median_axis_0(X):
"""Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : CSC sparse matrix, shape (n_samples, n_features)
Input data.
Returns
-------
median : ndarray, shape (n_features,)
Median.
"""
if not isinstance(X, sp.csc_matrix):
raise TypeError("Expected matrix of CSC format, got %s" % X.format)
indptr = X.indptr
n_samples, n_features = X.shape
median = np.zeros(n_features)
for f_ind, (start, end) in enumerate(zip(indptr[:-1], indptr[1:])):
# Prevent modifying X in place
data = np.copy(X.data[start: end])
nz = n_samples - data.size
median[f_ind] = _get_median(data, nz)
return median
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